Use of prior knowledge to inform restoration projects in estuaries of GOM

July 28, 2017

# randomize author order
aut <- c('Marcus Beck', 'Kirsten Dorans', 'Jessica Renee Henkel', 'Kathryn Ireland', 'Ed Sherwood', 'Patricia Varela') %>% 
  sample %>% 
  paste(collapse = ', ')

By Kirsten Dorans, Jessica Renee Henkel, Patricia Varela, Ed Sherwood, Kathryn Ireland, Marcus Beck

Deepwater Horizon Settlement Agreement

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Over $10B in Potential Restoration Activities

Graphic: eli-ocean.org

Cumulative Effects of Restoration Activities?

  • Despite considerable investments in aquatic ecosystem restoration, consistent and comprehensive effectiveness evaluation continues to elude practitioners at geographic scales. (Diefenderfer et al. 2016)

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A Network

Tampa Bay was gross

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Tampa Bay is a lot better now

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But how much less gross and why??

Can we use disparate data to prioritize future restoration projects aimed at improving water quality?
  • Synthesize data in space and time to evaluate cumulative effects of restoration projcts

  • Apply a Bayesian Decision Network with empirical observations to evaluate likelihood of potential outcomes

  • Expand the scope of analysis to alternative systems using a generalized framework

Water Quality Monitoring in Tampa Bay

  • Rich WQ Monitoring Datatset (1974-)
    • 45 Stations in TB
    • Chlorophyll, salinity, dissolved oxygen, etc.
    • Depth-integrated
    • QAQC
  • Time series, monthly step - ~500 obs. per site
  • Available as an EXCEL spreadsheet ftp://ftp.epchc.org

Tampa Bay Restoration Sites: Various Sources of Info

  • “Softer” Restoration -> Local ordinances (e.g. ferilizer restrictions), Education, etc.
  • “Soft” Restoration -> Habitat Creation, Enhancement and Management/Protection Measures
  • “Hard” Restoration -> Stormwater BMPs, Point Source Reductions through Time, Regulations

Tampa Bay Restoration Site Info: First Source

Tampa Bay Restoration Site Info: Second Source

  • Tracking “traditional” restoration sites since ~1990s
  • Include habitat creation, enhancement and acquisitions
  • http://apdb.tbeptech.org

Other Option

  • Restoration sites in Tampa Bay, watershed
    • Habitat Establishment
    • Habitat Enhancement
    • Habitat Protection
    • Stormwater Controls
    • Point Source Controls
  • 571 projects, 1971 - 2016

Overall Workflow

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Developing Restoration Dataset

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Data plyring

  • Can we identify a change in water quality from restoration?
  • What data do we have?
  • Can we plyr the data to identify a signal?
  • Can we plyr the data as input to a BN?

Data plyring

WQ and restoration sites

  • Can we plyr the data to identify a signal?
  • How can continuous water quality be linked to discrete restoration activites?

Data plyring

WQ and restoration sites

  • Can we plyr the data to identify a signal?
  • How can continuous water quality be linked to discrete restoration activites?
  • Consider an effect of restoration site type?

Data plyring

WQ and restoration sites

  • Can we plyr the data to identify a signal?
  • How can continuous water quality be linked to discrete restoration activites?
  • Consider an effect of restoration site type?
  • Consider distance of sites from water quality stations?

Data plyring

WQ and restoration sites

  • Can we plyr the data to identify a signal?
  • How can continuous water quality be linked to discrete restoration activites?
  • Consider an effect of restoration site type?
  • Consider distance of sites from water quality stations?
  • Consider a cumulative effect?

Data plyring

WQ and restoration sites

  • Can we plyr the data to identify a signal?
  • How can continuous water quality be linked to discrete restoration activites?
  • Consider an effect of restoration site type?
  • Consider distance of sites from water quality stations?
  • Consider a cumulative effect?

Data plyring

WQ and restoration sites: Spatial match

Data plyring

WQ and restoration sites: Spatial match

WQ and restoration sites: Temporal match

Data plyring

WQ and restoration sites: Spatial match

WQ and restoration sites: Temporal match, before/after

Data plyring

WQ and restoration sites: Spatial match

WQ and restoration sites: Temporal match, before/after, slice

Data plyring

What do the data look like? For one water quality station matched to many restoration sites…

WQ and restoration sites: Temporal match, before/after, slice

# A tibble: 4 x 3
# Groups:   stat [1]
   stat     cmb     cval
  <int>   <chr>    <dbl>
1     7 hab_aft 8.255185
2     7 hab_bef 8.350187
3     7 wtr_aft 8.053273
4     7 wtr_bef 8.129733

Data plyring

What do the data look like? For one water quality station matched to many restoration sites…

WQ and restoration sites: Temporal match, before/after, slice

# A tibble: 4 x 4
   stat     hab     wtr     cval
  <int>  <fctr>  <fctr>    <dbl>
1     7 hab_aft wtr_aft 8.154229
2     7 hab_aft wtr_bef 8.192459
3     7 hab_bef wtr_aft 8.201730
4     7 hab_bef wtr_bef 8.239960

Data plyring

What do the data look like? For many water quality station matched to many restoration sites…

# A tibble: 20 x 4
    stat     hab     wtr      cval
   <int>  <fctr>  <fctr>     <dbl>
 1     6 hab_aft wtr_aft  8.903273
 2     6 hab_aft wtr_bef 11.720206
 3     6 hab_bef wtr_aft 11.902951
 4     6 hab_bef wtr_bef 14.719883
 5     7 hab_aft wtr_aft  8.154229
 6     7 hab_aft wtr_bef  8.192459
 7     7 hab_bef wtr_aft  8.201730
 8     7 hab_bef wtr_bef  8.239960
 9     8 hab_aft wtr_aft 19.867100
10     8 hab_aft wtr_bef 17.444274
11     8 hab_bef wtr_aft 17.331973
12     8 hab_bef wtr_bef 14.909147
13     9 hab_aft wtr_aft  9.030021
14     9 hab_aft wtr_bef  8.621069
15     9 hab_bef wtr_aft  8.398558
16     9 hab_bef wtr_bef  7.989606
17    11 hab_aft wtr_aft  6.576058
18    11 hab_aft wtr_bef  6.727664
19    11 hab_bef wtr_aft  8.112902
20    11 hab_bef wtr_bef  8.264508

Data plyring

What do the data look like? For many water quality station matched to many restoration sites…

Data plyring

What do the data look like? For many water quality stations matched to many restoration sites…

Data plyring

What do the data look like? For many water quality stations matched to many restoration sites…

Data plyring

  • In other words, what is the conditional distribution of chlorophyll given restoration type and before/after effect?

  • Similar to a two-way ANOVA…

\[ Chl \sim\ f\left(Water \space\ treatment \times Habitat \space\ restoration\right) \]

  • This can be extrapolated to additional 'treatments', or a three-way ANOVA

\[ Chl \sim\ f\left(Water \space\ treatment \times Habitat \space\ restoration \times Salinity \right) \]

Data plyring

Conditional distributions on two-levels:

Data plyring

Conditional distributions on three-levels:

Data plyring

Conditional distributions on three-levels:

Data plyring

Conditional distributions on three-levels:

Bayesian Network

  • Water quality (chlorophyll) responds to restoration with varying effects by salinity

  • In the frequentist framework - mean chlorophyll varies given treatment

\[ Chl \sim\ f\left(Water \space\ treatment \times Habitat \space\ restoration \times Salinity \right) \]

  • In the Bayesian framework - probability of an event depends on occurrence of other events

\[ P\left(Chl \mid Event\right) = \frac{P\left(Event \mid Chl\right) \cdot P\left(Chl \right)}{P \left(Event\right)} \]

Bayesian Network

What is the probability of low/medium/high chlorophyll given other events?

  • Do water quality conditions differ by restoration type?
  • Does it differ by salinity as a natural covariate?
  • Is the change in agreement with expectation?

BN lets us evaluate likelihood of potential outcomes given conditional distributions

Conclusion

  • Next steps (all)

Acknowledgments